{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication\Log\4_1_Figures_ISSP.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}29 Aug 2024, 15:43:51

{com}. do "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication\Do\4_1_Figures_ISSP.do"
{txt}
{com}. *****************************************************************************
. *                                 Figures Descriptives with ISSP                                        *
. *                                                                                                                                                       *                       
. * Author:                       Valentina Gonzalez Rostani                                                      *
. * Contact:                      mag384@pitt.edu                                                                 *
. * Date:                         August 9 2024                                                                                   *
. * Version:                      Stata 17                                                                                                *                                                                          
. *                                                                                                                                                       *
. *****************************************************************************
. /*
> This do-file:
> - Creates Figure 1, 2 and A3 using data from ISSP. 
> 
> Input:
> - Data\Figures_ISSP.dta
> 
> Output:
> - Figure 1: Relative Share of Labor Force 1995 to 2014 [Figures\Relative Share of Labor Force 1998 to 2014 ISSP.pdf]
> - Figure 2: Electoral consequences, Routine and Non-Routine Voters [Figure/price_by_mpg.pdf]
> - Figure A3: Importance of job security, Difficulties to find a new job, Concerns about losing the job and Job dissatisfaction [Figure/jobdisatisfactionpredictedtogetherall.pdf]
> */
. 
. *Defining Directory
. cd "C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication"
{res}C:\Users\vgonz\Dropbox\Pitt\OneDrive for Business\Dissertation - Vale\Paper 2 - Political-Economic Polarization\Replication
{txt}
{com}. 
. *Calling the data
. use "Data\Figures_ISSP.dta", clear 
{txt}
{com}. 
. *******************************************************************************
. * Graphs
. *******************************************************************************
. * Graph style for F1 & 2
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, box
. grstyle color background white
. grstyle color major_grid gs8
. grstyle linepattern major_grid dot
. {c )-}
{txt}
{com}. // Figure 1: Relative Share of Labor Force 1995 to 2014
. {c -(}
.     * Generating summary statistics with three task categories:
.     * Task 1, Task 2, and Task 33 following Autor (2003) and coded by Kurer and Gallego (2019)
.     
.     * Preserve the current dataset in memory so it can be restored later
.     preserve 
.     
.     * Collapse the data to calculate the mean of task1, task2, and task33 weighted by 'weight' for each year
.     collapse task1 task2 task33 [aw=weight], by(year)
{res}{com}.     
.     * Keep only the observations where the year is greater than 1997 to look post automation shock
.     keep if year > 1997
{txt}(3 observations deleted)
{com}.     
.     * Create a line graph for the task categories over time
.     * The first line represents Non-Routine Cognitive tasks (green line)
.  graph twoway line  task1 year if year>1997, lc(green) legend(label(1 "Non-Routine Cognitive")) || ///
>   line  task2 year if year>1997, lc(red) legend(label(2 "Routine")) || ///
>     line  task33 year if year>1997, lc(blue) legend(label(3 "Non-Routine Manual")) 
{res}{com}. 
.     
.     * Export the graph as a PDF file with the specified name, replacing any existing file
.     graph export "Figure\Relative Share of Labor Force 1998 to 2014 ISSP.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure\Relative Share of Labor Force 1998 to 2014 ISSP.pdf{rm}
saved as
PDF
format
{p_end}
{com}.     
.     * Restore the original dataset that was in memory before the collapse
.     restore
. {c )-}
{txt}
{com}. 
. // Figure 2: Electoral consequences, Routine and Non-Routine Voters
. {c -(}
. * Here I look at the share of votes by party family, and instead of using the three categories of risks, I put together non-routine manual and routine. I repeat this for RR, ML, MR, Non-voters. Code commented in the first one. 
. // Radical right (graph commented)
. {c -(}
. * Label the variable 'radicalR' to represent "Votes for Right Populist (%)"
. lab var radicalR "Votes for Right Populist (%)"
. 
. * Preserve the current dataset in memory so it can be restored later
. preserve        
. 
. * Generate a new variable 'task2and3' initialized to 1 if 'task2' equals 1
. gen task2and3 = 1 if task2 == 1
{txt}(162,633 missing values generated)
{com}. 
. * Replace 'task2and3' with 1 if 'task33' equals 1 (combining task2 and task33)
. replace task2and3 = 1 if task33 == 1
{txt}(43,947 real changes made)
{com}. 
. * Replace 'task2and3' with 0 if 'task1' equals 1 (Non-Routine Cognitive tasks)
. replace task2and3 = 0 if task1 == 1
{txt}(71,529 real changes made)
{com}. 
. * Keep only observations where 'emplB' is less than 6 (likely filtering by employment status or category)
. keep if emplB < 6
{txt}(17,803 observations deleted)
{com}. 
. * Collapse the data to calculate the mean of 'radicalR' weighted by 'weight' for each year, task1, and task2and3 categories
. collapse radicalR [aw=weight], by(year task1 task2and3)
{res}{com}. 
. * Create a line graph for 'radicalR' over time, segmented by task categories
. graph twoway line  radicalR year if task1==1, lc(green) legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  radicalR year if task2and3==1, lc(red) legend(label(2 "Routine & Manual")) ytitle("Votes for Right Populist (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. 
. * Save the graph as "RR.gph" in the "Figure" directory, replacing any existing file with the same name
. graph save "Figure/RR.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/RR.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/RR.gph} saved
{com}. 
. * Restore the original dataset that was in memory before the modifications
. restore
. {c )-}
. // Maintream left
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse mainstreamleft [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  mainstreamleft year if task1==1, lc(green)   legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  mainstreamleft year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))  ytitle("Votes for Mainstream Left (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/ML.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/ML.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/ML.gph} saved
{com}. 
. restore 
. {c )-}
. // Mainstream right
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse mainstreamright [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  mainstreamright year if task1==1, lc(green)  legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  mainstreamright year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))  ytitle("Votes for Mainstream Right (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/MR.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/MR.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/MR.gph} saved
{com}. 
. restore 
. {c )-}
. // Non Voters
. {c -(}
.  preserve       
. 
. gen task2and3=1 if task2==1
{txt}(162,633 missing values generated)
{com}. replace task2and3=1 if task33==1
{txt}(43,947 real changes made)
{com}. replace task2and3=0 if task1==1
{txt}(71,529 real changes made)
{com}. 
. keep if emplB<6
{txt}(17,803 observations deleted)
{com}. collapse nonvoters [aw=weight], by(year task1 task2and3)
{res}{com}. 
. graph twoway line  nonvoters year if task1==1, lc(green)  legend(label(1 "Non-Routine Cognitive"))  || ///
>   line  nonvoters year if task2and3==1, lc(red) legend(label(2 "Routine & Manual"))   ytitle("Non-Voters (%)", size(small))    legend(size(vsmall))  xtitle("Year", size(small))   ylabel(, labsize(vsmall))  xlabel(, labsize(vsmall))
{res}{com}. graph save "Figure/Nv.gph", replace
{txt}{p 0 4 2}
(file {bf}
Figure/Nv.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/Nv.gph} saved
{com}. 
. restore 
. {c )-}
.  * Combine multiple graphs into a single figure
. graph combine "Figure/ML.gph" "Figure/MR.gph" "Figure/RR.gph" "Figure/Nv.gph"
{res}{com}. * Export the combined graph as a PDF file named "price_by_mpg.pdf" in the "Figure" directory
. graph export "Figure/price_by_mpg.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure/price_by_mpg.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. * Erase (delete) the individual graph files from the "Figure" directory after combining them
. erase "Figure/ML.gph"
. erase "Figure/MR.gph"
. erase "Figure/RR.gph"
. erase "Figure/Nv.gph"
. 
. 
. 
. {c )-}
{txt}
{com}. // Figure A3: Importance of job security, Difficulties to find a new job, Concerns about losing the job and Job dissatisfaction
. {c -(}
. * The code categorizes individuals based on whether they find it difficult to get a new job, then runs a logistic regression to assess how the risk of automation influences this difficulty, adjusting for weights. It calculates the predicted probabilities of job difficulty across different levels of automation risk and generates a graph to visualize these probabilities, saving the result as a file.
. * Code commented for first graph whether it is difficult to find a new job. Then code for the other three is similar (levels of variables changes so the dummies are different). 
. 
. 
. * Graph style for figure A3
. {c -(}
. grstyle clear
. set scheme s2color
. grstyle init
{res}{com}. grstyle set plain, nogrid
. grstyle color background white
. {c )-}
. * W_easynewjob
. {c -(}
. * Generate a new variable 'W_easynewjob_difficult' to categorize the difficulty of finding a new job
. * Set 'W_easynewjob_difficult' to 1 if 'W_easynewjob' is 4 or 5 (indicating it is difficult)
. gen W_easynewjob_difficult = 1 if W_easynewjob == 4 | W_easynewjob == 5
{txt}(189,640 missing values generated)
{com}. * Replace 'W_easynewjob_difficult' with 0 if 'W_easynewjob' is 1, 2, or 3 (indicating it is not difficult)
. replace W_easynewjob_difficult = 0 if W_easynewjob == 1 | W_easynewjob == 2 | W_easynewjob == 3
{txt}(12,317 real changes made)
{com}. 
. * Run a logistic regression model with 'W_easynewjob_difficult' as the dependent variable
. logit W_easynewjob_difficult rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -14737.17}  
Iteration 1:{space 3}log pseudolikelihood = {res:-14666.246}  
Iteration 2:{space 3}log pseudolikelihood = {res:-14666.231}  
Iteration 3:{space 3}log pseudolikelihood = {res:-14666.231}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:126.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-14666.231}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0048}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}W_easynewjob_difficult{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}rti {c |}{col 24}{res}{space 2} .1619526{col 36}{space 2} .0144131{col 47}{space 1}   11.24{col 56}{space 3}0.000{col 64}{space 4} .1337034{col 77}{space 3} .1902019
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .1418846{col 36}{space 2} .0145724{col 47}{space 1}    9.74{col 56}{space 3}0.000{col 64}{space 4} .1133232{col 77}{space 3} .1704461
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
. * Calculate the marginal effects of 'rti' on the predicted probability of 'W_easynewjob_difficult'
. * atmeans calculates the marginal effect at the means of the covariates
. * at() specifies a range of values for 'rti' from -1.52 to 2.24 with increments of 0.05
. margins, atmeans at(rti=(-1.52(0.05)2.24))
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_easynewjob_difficult), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4739528{col 26}{space 2} .0062495{col 37}{space 1}   75.84{col 46}{space 3}0.000{col 54}{space 4}  .461704{col 67}{space 3} .4862016
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4759721{col 26}{space 2} .0061063{col 37}{space 1}   77.95{col 46}{space 3}0.000{col 54}{space 4}  .464004{col 67}{space 3} .4879402
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4779922{col 26}{space 2} .0059646{col 37}{space 1}   80.14{col 46}{space 3}0.000{col 54}{space 4} .4663018{col 67}{space 3} .4896827
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .480013{col 26}{space 2} .0058247{col 37}{space 1}   82.41{col 46}{space 3}0.000{col 54}{space 4} .4685969{col 67}{space 3} .4914292
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4820345{col 26}{space 2} .0056866{col 37}{space 1}   84.77{col 46}{space 3}0.000{col 54}{space 4} .4708891{col 67}{space 3}   .49318
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4840566{col 26}{space 2} .0055505{col 37}{space 1}   87.21{col 46}{space 3}0.000{col 54}{space 4} .4731778{col 67}{space 3} .4949354
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .4860792{col 26}{space 2} .0054167{col 37}{space 1}   89.74{col 46}{space 3}0.000{col 54}{space 4} .4754627{col 67}{space 3} .4966957
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4881023{col 26}{space 2} .0052853{col 37}{space 1}   92.35{col 46}{space 3}0.000{col 54}{space 4} .4777433{col 67}{space 3} .4984613
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4901257{col 26}{space 2} .0051566{col 37}{space 1}   95.05{col 46}{space 3}0.000{col 54}{space 4}  .480019{col 67}{space 3} .5002324
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{txt}{space 9}76  {c |}{col 14}{res}{space 2} .6231732{col 26}{space 2}  .008594{col 37}{space 1}   72.51{col 46}{space 3}0.000{col 54}{space 4} .6063293{col 67}{space 3} .6400171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}. 
. * Plot the margins with a line graph and display the confidence intervals as dotted lines
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,420}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_easynewjob_difficult), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4739528{col 26}{space 2} .0062495{col 37}{space 1}   75.84{col 46}{space 3}0.000{col 54}{space 4}  .461704{col 67}{space 3} .4862016
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4759721{col 26}{space 2} .0061063{col 37}{space 1}   77.95{col 46}{space 3}0.000{col 54}{space 4}  .464004{col 67}{space 3} .4879402
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4779922{col 26}{space 2} .0059646{col 37}{space 1}   80.14{col 46}{space 3}0.000{col 54}{space 4} .4663018{col 67}{space 3} .4896827
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .480013{col 26}{space 2} .0058247{col 37}{space 1}   82.41{col 46}{space 3}0.000{col 54}{space 4} .4685969{col 67}{space 3} .4914292
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4820345{col 26}{space 2} .0056866{col 37}{space 1}   84.77{col 46}{space 3}0.000{col 54}{space 4} .4708891{col 67}{space 3}   .49318
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4840566{col 26}{space 2} .0055505{col 37}{space 1}   87.21{col 46}{space 3}0.000{col 54}{space 4} .4731778{col 67}{space 3} .4949354
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .4860792{col 26}{space 2} .0054167{col 37}{space 1}   89.74{col 46}{space 3}0.000{col 54}{space 4} .4754627{col 67}{space 3} .4966957
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4881023{col 26}{space 2} .0052853{col 37}{space 1}   92.35{col 46}{space 3}0.000{col 54}{space 4} .4777433{col 67}{space 3} .4984613
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4901257{col 26}{space 2} .0051566{col 37}{space 1}   95.05{col 46}{space 3}0.000{col 54}{space 4}  .480019{col 67}{space 3} .5002324
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .4921495{col 26}{space 2} .0050308{col 37}{space 1}   97.83{col 46}{space 3}0.000{col 54}{space 4} .4822894{col 67}{space 3} .5020096
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4941735{col 26}{space 2} .0049081{col 37}{space 1}  100.69{col 46}{space 3}0.000{col 54}{space 4} .4845539{col 67}{space 3} .5037932
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4961977{col 26}{space 2} .0047888{col 37}{space 1}  103.62{col 46}{space 3}0.000{col 54}{space 4} .4868118{col 67}{space 3} .5055837
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4982221{col 26}{space 2} .0046733{col 37}{space 1}  106.61{col 46}{space 3}0.000{col 54}{space 4} .4890626{col 67}{space 3} .5073815
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5002465{col 26}{space 2} .0045618{col 37}{space 1}  109.66{col 46}{space 3}0.000{col 54}{space 4} .4913056{col 67}{space 3} .5091874
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5022709{col 26}{space 2} .0044546{col 37}{space 1}  112.75{col 46}{space 3}0.000{col 54}{space 4}   .49354{col 67}{space 3} .5110017
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .5042952{col 26}{space 2} .0043521{col 37}{space 1}  115.87{col 46}{space 3}0.000{col 54}{space 4} .4957652{col 67}{space 3} .5128251
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5063194{col 26}{space 2} .0042547{col 37}{space 1}  119.00{col 46}{space 3}0.000{col 54}{space 4} .4979804{col 67}{space 3} .5146583
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5083433{col 26}{space 2} .0041626{col 37}{space 1}  122.12{col 46}{space 3}0.000{col 54}{space 4} .5001847{col 67}{space 3} .5165019
{txt}{space 9}19  {c |}{col 14}{res}{space 2}  .510367{col 26}{space 2} .0040764{col 37}{space 1}  125.20{col 46}{space 3}0.000{col 54}{space 4} .5023774{col 67}{space 3} .5183566
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{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5144133{col 26}{space 2} .0039229{col 37}{space 1}  131.13{col 46}{space 3}0.000{col 54}{space 4} .5067247{col 67}{space 3}  .522102
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5164358{col 26}{space 2} .0038563{col 37}{space 1}  133.92{col 46}{space 3}0.000{col 54}{space 4} .5088776{col 67}{space 3}  .523994
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5184577{col 26}{space 2} .0037971{col 37}{space 1}  136.54{col 46}{space 3}0.000{col 54}{space 4} .5110156{col 67}{space 3} .5258999
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5204791{col 26}{space 2} .0037455{col 37}{space 1}  138.96{col 46}{space 3}0.000{col 54}{space 4} .5131381{col 67}{space 3} .5278201
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5224997{col 26}{space 2} .0037019{col 37}{space 1}  141.14{col 46}{space 3}0.000{col 54}{space 4} .5152442{col 67}{space 3} .5297553
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5245197{col 26}{space 2} .0036665{col 37}{space 1}  143.06{col 46}{space 3}0.000{col 54}{space 4} .5173335{col 67}{space 3} .5317059
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5265388{col 26}{space 2} .0036396{col 37}{space 1}  144.67{col 46}{space 3}0.000{col 54}{space 4} .5194053{col 67}{space 3} .5336723
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5285571{col 26}{space 2} .0036213{col 37}{space 1}  145.96{col 46}{space 3}0.000{col 54}{space 4} .5214594{col 67}{space 3} .5356547
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .5305744{col 26}{space 2} .0036118{col 37}{space 1}  146.90{col 46}{space 3}0.000{col 54}{space 4} .5234953{col 67}{space 3} .5376534
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .5325907{col 26}{space 2} .0036111{col 37}{space 1}  147.49{col 46}{space 3}0.000{col 54}{space 4} .5255131{col 67}{space 3} .5396684
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .534606{col 26}{space 2} .0036192{col 37}{space 1}  147.72{col 46}{space 3}0.000{col 54}{space 4} .5275125{col 67}{space 3} .5416994
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5366201{col 26}{space 2} .0036359{col 37}{space 1}  147.59{col 46}{space 3}0.000{col 54}{space 4} .5294938{col 67}{space 3} .5437464
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .5386331{col 26}{space 2} .0036612{col 37}{space 1}  147.12{col 46}{space 3}0.000{col 54}{space 4} .5314572{col 67}{space 3} .5458089
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .5406447{col 26}{space 2} .0036948{col 37}{space 1}  146.33{col 46}{space 3}0.000{col 54}{space 4}  .533403{col 67}{space 3} .5478864
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .5426551{col 26}{space 2} .0037365{col 37}{space 1}  145.23{col 46}{space 3}0.000{col 54}{space 4} .5353317{col 67}{space 3} .5499785
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5446641{col 26}{space 2} .0037859{col 37}{space 1}  143.87{col 46}{space 3}0.000{col 54}{space 4} .5372439{col 67}{space 3} .5520843
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .5466716{col 26}{space 2} .0038427{col 37}{space 1}  142.26{col 46}{space 3}0.000{col 54}{space 4}   .53914{col 67}{space 3} .5542032
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .5486776{col 26}{space 2} .0039066{col 37}{space 1}  140.45{col 46}{space 3}0.000{col 54}{space 4} .5410208{col 67}{space 3} .5563343
{txt}{space 9}39  {c |}{col 14}{res}{space 2}  .550682{col 26}{space 2} .0039771{col 37}{space 1}  138.46{col 46}{space 3}0.000{col 54}{space 4} .5428871{col 67}{space 3} .5584769
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5526848{col 26}{space 2} .0040538{col 37}{space 1}  136.34{col 46}{space 3}0.000{col 54}{space 4} .5447394{col 67}{space 3} .5606302
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5546858{col 26}{space 2} .0041365{col 37}{space 1}  134.10{col 46}{space 3}0.000{col 54}{space 4} .5465785{col 67}{space 3} .5627931
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5566851{col 26}{space 2} .0042245{col 37}{space 1}  131.77{col 46}{space 3}0.000{col 54}{space 4} .5484052{col 67}{space 3}  .564965
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .5586826{col 26}{space 2} .0043177{col 37}{space 1}  129.39{col 46}{space 3}0.000{col 54}{space 4} .5502201{col 67}{space 3}  .567145
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5606782{col 26}{space 2} .0044155{col 37}{space 1}  126.98{col 46}{space 3}0.000{col 54}{space 4}  .552024{col 67}{space 3} .5693323
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5626718{col 26}{space 2} .0045176{col 37}{space 1}  124.55{col 46}{space 3}0.000{col 54}{space 4} .5538174{col 67}{space 3} .5715262
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5646633{col 26}{space 2} .0046238{col 37}{space 1}  122.12{col 46}{space 3}0.000{col 54}{space 4} .5556009{col 67}{space 3} .5737257
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5666528{col 26}{space 2} .0047335{col 37}{space 1}  119.71{col 46}{space 3}0.000{col 54}{space 4} .5573753{col 67}{space 3} .5759304
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5686402{col 26}{space 2} .0048467{col 37}{space 1}  117.33{col 46}{space 3}0.000{col 54}{space 4} .5591409{col 67}{space 3} .5781395
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5706253{col 26}{space 2} .0049629{col 37}{space 1}  114.98{col 46}{space 3}0.000{col 54}{space 4} .5608983{col 67}{space 3} .5803524
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .5726082{col 26}{space 2} .0050818{col 37}{space 1}  112.68{col 46}{space 3}0.000{col 54}{space 4}  .562648{col 67}{space 3} .5825684
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5745887{col 26}{space 2} .0052033{col 37}{space 1}  110.43{col 46}{space 3}0.000{col 54}{space 4} .5643904{col 67}{space 3} .5847871
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5765669{col 26}{space 2} .0053271{col 37}{space 1}  108.23{col 46}{space 3}0.000{col 54}{space 4} .5661259{col 67}{space 3} .5870079
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5785426{col 26}{space 2}  .005453{col 37}{space 1}  106.10{col 46}{space 3}0.000{col 54}{space 4} .5678549{col 67}{space 3} .5892303
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5805158{col 26}{space 2} .0055808{col 37}{space 1}  104.02{col 46}{space 3}0.000{col 54}{space 4} .5695777{col 67}{space 3} .5914539
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5824864{col 26}{space 2} .0057102{col 37}{space 1}  102.01{col 46}{space 3}0.000{col 54}{space 4} .5712946{col 67}{space 3} .5936782
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .5844544{col 26}{space 2} .0058412{col 37}{space 1}  100.06{col 46}{space 3}0.000{col 54}{space 4} .5730059{col 67}{space 3} .5959029
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5864197{col 26}{space 2} .0059735{col 37}{space 1}   98.17{col 46}{space 3}0.000{col 54}{space 4} .5747119{col 67}{space 3} .5981275
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5883822{col 26}{space 2}  .006107{col 37}{space 1}   96.35{col 46}{space 3}0.000{col 54}{space 4} .5764128{col 67}{space 3} .6003517
{txt}{space 9}59  {c |}{col 14}{res}{space 2}  .590342{col 26}{space 2} .0062415{col 37}{space 1}   94.58{col 46}{space 3}0.000{col 54}{space 4} .5781088{col 67}{space 3} .6025752
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .5922988{col 26}{space 2}  .006377{col 37}{space 1}   92.88{col 46}{space 3}0.000{col 54}{space 4} .5798001{col 67}{space 3} .6047976
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5942528{col 26}{space 2} .0065133{col 37}{space 1}   91.24{col 46}{space 3}0.000{col 54}{space 4} .5814869{col 67}{space 3} .6070187
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5962038{col 26}{space 2} .0066503{col 37}{space 1}   89.65{col 46}{space 3}0.000{col 54}{space 4} .5831694{col 67}{space 3} .6092382
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .5981517{col 26}{space 2} .0067879{col 37}{space 1}   88.12{col 46}{space 3}0.000{col 54}{space 4} .5848476{col 67}{space 3} .6114558
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .6000966{col 26}{space 2}  .006926{col 37}{space 1}   86.64{col 46}{space 3}0.000{col 54}{space 4} .5865218{col 67}{space 3} .6136713
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .6020382{col 26}{space 2} .0070645{col 37}{space 1}   85.22{col 46}{space 3}0.000{col 54}{space 4} .5881921{col 67}{space 3} .6158844
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6039767{col 26}{space 2} .0072033{col 37}{space 1}   83.85{col 46}{space 3}0.000{col 54}{space 4} .5898585{col 67}{space 3}  .618095
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6059119{col 26}{space 2} .0073424{col 37}{space 1}   82.52{col 46}{space 3}0.000{col 54}{space 4} .5915211{col 67}{space 3} .6203028
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6078439{col 26}{space 2} .0074816{col 37}{space 1}   81.25{col 46}{space 3}0.000{col 54}{space 4} .5931802{col 67}{space 3} .6225075
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6097724{col 26}{space 2} .0076209{col 37}{space 1}   80.01{col 46}{space 3}0.000{col 54}{space 4} .5948356{col 67}{space 3} .6247092
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6116975{col 26}{space 2} .0077603{col 37}{space 1}   78.82{col 46}{space 3}0.000{col 54}{space 4} .5964876{col 67}{space 3} .6269074
{txt}{space 9}71  {c |}{col 14}{res}{space 2} .6136191{col 26}{space 2} .0078996{col 37}{space 1}   77.68{col 46}{space 3}0.000{col 54}{space 4} .5981361{col 67}{space 3} .6291021
{txt}{space 9}72  {c |}{col 14}{res}{space 2} .6155372{col 26}{space 2} .0080389{col 37}{space 1}   76.57{col 46}{space 3}0.000{col 54}{space 4} .5997813{col 67}{space 3} .6312931
{txt}{space 9}73  {c |}{col 14}{res}{space 2} .6174517{col 26}{space 2}  .008178{col 37}{space 1}   75.50{col 46}{space 3}0.000{col 54}{space 4} .6014232{col 67}{space 3} .6334803
{txt}{space 9}74  {c |}{col 14}{res}{space 2} .6193626{col 26}{space 2} .0083169{col 37}{space 1}   74.47{col 46}{space 3}0.000{col 54}{space 4} .6030618{col 67}{space 3} .6356635
{txt}{space 9}75  {c |}{col 14}{res}{space 2} .6212698{col 26}{space 2} .0084556{col 37}{space 1}   73.47{col 46}{space 3}0.000{col 54}{space 4} .6046972{col 67}{space 3} .6378424
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .6231732{col 26}{space 2}  .008594{col 37}{space 1}   72.51{col 46}{space 3}0.000{col 54}{space 4} .6063293{col 67}{space 3} .6400171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Job dissatisfaction")  saving("Figure/difficult.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/difficult.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/difficult.gph} saved
{com}. {c )-}
. 
.          
. * W_satisfaction
. {c -(}
. gen dissatisfied=1 if W_satisfaction==5 | W_satisfaction==6 | W_satisfaction==7
{txt}(202,467 missing values generated)
{com}. replace dissatisfied=0 if W_satisfaction==1 | W_satisfaction==2 | W_satisfaction==3 | W_satisfaction==4
{txt}(25,253 real changes made)
{com}. 
. 
. logit dissatisfied rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5231.0894}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5216.5255}  
Iteration 2:{space 3}log pseudolikelihood = {res:-5216.3905}  
Iteration 3:{space 3}log pseudolikelihood = {res:-5216.3905}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,845}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:27.37}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-5216.3905}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0028}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}dissatisfied{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .1403051{col 26}{space 2} .0268173{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4} .0877442{col 67}{space 3} .1928661
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.663969{col 26}{space 2} .0298265{col 37}{space 1}  -89.32{col 46}{space 3}0.000{col 54}{space 4}-2.722428{col 67}{space 3} -2.60551
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,845}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(dissatisfied), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Job dissatisfaction")  saving("Figure/dissatisfied.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/dissatisfied.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/dissatisfied.gph} saved
{com}. {c )-}        
. 
.          
. * W_losing
. {c -(}
. gen W_losing2=1 if W_losing==1 | W_losing==2 | W_losing==3
{txt}(189,775 missing values generated)
{com}. replace W_losing2=0 if W_losing==4 
{txt}(12,463 real changes made)
{com}. 
. 
. logit W_losing2   rti [pweight=weight]

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-14884.364}  
Iteration 1:{space 3}log pseudolikelihood = {res:-14852.506}  
Iteration 2:{space 3}log pseudolikelihood = {res:-14852.502}  
Iteration 3:{space 3}log pseudolikelihood = {res:-14852.502}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,764}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:56.57}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-14852.502}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0021}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   W_losing2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .1077349{col 26}{space 2} .0143236{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4} .0796612{col 67}{space 3} .1358087
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2321113{col 26}{space 2} .0144658{col 37}{space 1}   16.05{col 46}{space 3}0.000{col 54}{space 4} .2037589{col 67}{space 3} .2604638
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}. 
.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:21,764}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_losing2), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
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{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5211161{col 26}{space 2}  .005775{col 37}{space 1}   90.24{col 46}{space 3}0.000{col 54}{space 4} .5097972{col 67}{space 3} .5324349
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5224602{col 26}{space 2} .0056348{col 37}{space 1}   92.72{col 46}{space 3}0.000{col 54}{space 4} .5114161{col 67}{space 3} .5335042
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .523804{col 26}{space 2} .0054969{col 37}{space 1}   95.29{col 46}{space 3}0.000{col 54}{space 4} .5130303{col 67}{space 3} .5345777
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5251474{col 26}{space 2} .0053614{col 37}{space 1}   97.95{col 46}{space 3}0.000{col 54}{space 4} .5146392{col 67}{space 3} .5356557
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5264905{col 26}{space 2} .0052286{col 37}{space 1}  100.69{col 46}{space 3}0.000{col 54}{space 4} .5162426{col 67}{space 3} .5367385
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .5278333{col 26}{space 2} .0050987{col 37}{space 1}  103.52{col 46}{space 3}0.000{col 54}{space 4}   .51784{col 67}{space 3} .5378266
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{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5318589{col 26}{space 2} .0047285{col 37}{space 1}  112.48{col 46}{space 3}0.000{col 54}{space 4} .5225912{col 67}{space 3} .5411266
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5331999{col 26}{space 2} .0046125{col 37}{space 1}  115.60{col 46}{space 3}0.000{col 54}{space 4} .5241596{col 67}{space 3} .5422401
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{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5398972{col 26}{space 2} .0041013{col 37}{space 1}  131.64{col 46}{space 3}0.000{col 54}{space 4} .5318587{col 67}{space 3} .5479357
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{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5914619{col 26}{space 2} .0059205{col 37}{space 1}   99.90{col 46}{space 3}0.000{col 54}{space 4} .5798578{col 67}{space 3} .6030659
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5927629{col 26}{space 2} .0060555{col 37}{space 1}   97.89{col 46}{space 3}0.000{col 54}{space 4} .5808944{col 67}{space 3} .6046314
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .5940625{col 26}{space 2} .0061916{col 37}{space 1}   95.95{col 46}{space 3}0.000{col 54}{space 4} .5819272{col 67}{space 3} .6061979
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .5953609{col 26}{space 2}  .006329{col 37}{space 1}   94.07{col 46}{space 3}0.000{col 54}{space 4} .5829564{col 67}{space 3} .6077654
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5966579{col 26}{space 2} .0064673{col 37}{space 1}   92.26{col 46}{space 3}0.000{col 54}{space 4} .5839822{col 67}{space 3} .6093336
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5979536{col 26}{space 2} .0066066{col 37}{space 1}   90.51{col 46}{space 3}0.000{col 54}{space 4} .5850049{col 67}{space 3} .6109023
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .5992479{col 26}{space 2} .0067467{col 37}{space 1}   88.82{col 46}{space 3}0.000{col 54}{space 4} .5860246{col 67}{space 3} .6124713
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .6005409{col 26}{space 2} .0068876{col 37}{space 1}   87.19{col 46}{space 3}0.000{col 54}{space 4} .5870414{col 67}{space 3} .6140403
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .6018324{col 26}{space 2} .0070291{col 37}{space 1}   85.62{col 46}{space 3}0.000{col 54}{space 4} .5880556{col 67}{space 3} .6156092
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6031225{col 26}{space 2} .0071713{col 37}{space 1}   84.10{col 46}{space 3}0.000{col 54}{space 4} .5890671{col 67}{space 3} .6171779
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6044112{col 26}{space 2} .0073139{col 37}{space 1}   82.64{col 46}{space 3}0.000{col 54}{space 4} .5900762{col 67}{space 3} .6187462
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6056984{col 26}{space 2}  .007457{col 37}{space 1}   81.23{col 46}{space 3}0.000{col 54}{space 4}  .591083{col 67}{space 3} .6203139
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6069842{col 26}{space 2} .0076005{col 37}{space 1}   79.86{col 46}{space 3}0.000{col 54}{space 4} .5920875{col 67}{space 3} .6218809
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6082685{col 26}{space 2} .0077443{col 37}{space 1}   78.54{col 46}{space 3}0.000{col 54}{space 4} .5930899{col 67}{space 3} .6234471
{txt}{space 9}71  {c |}{col 14}{res}{space 2} .6095513{col 26}{space 2} .0078885{col 37}{space 1}   77.27{col 46}{space 3}0.000{col 54}{space 4} .5940902{col 67}{space 3} .6250124
{txt}{space 9}72  {c |}{col 14}{res}{space 2} .6108326{col 26}{space 2} .0080328{col 37}{space 1}   76.04{col 46}{space 3}0.000{col 54}{space 4} .5950886{col 67}{space 3} .6265766
{txt}{space 9}73  {c |}{col 14}{res}{space 2} .6121123{col 26}{space 2} .0081773{col 37}{space 1}   74.85{col 46}{space 3}0.000{col 54}{space 4}  .596085{col 67}{space 3} .6281396
{txt}{space 9}74  {c |}{col 14}{res}{space 2} .6133905{col 26}{space 2}  .008322{col 37}{space 1}   73.71{col 46}{space 3}0.000{col 54}{space 4} .5970797{col 67}{space 3} .6297014
{txt}{space 9}75  {c |}{col 14}{res}{space 2} .6146672{col 26}{space 2} .0084668{col 37}{space 1}   72.60{col 46}{space 3}0.000{col 54}{space 4} .5980725{col 67}{space 3} .6312618
{txt}{space 9}76  {c |}{col 14}{res}{space 2} .6159422{col 26}{space 2} .0086117{col 37}{space 1}   71.52{col 46}{space 3}0.000{col 54}{space 4} .5990637{col 67}{space 3} .6328208
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Concerns about losing job") saving("Figure/losing.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/losing.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/losing.gph} saved
{com}. {c )-}
. 
. * W_jobsec
. {c -(}
. gen W_jobsec2=1 if W_jobsec==1 | W_jobsec==2
{txt}(174,934 missing values generated)
{com}. replace W_jobsec2=0 if W_jobsec==3 | W_jobsec==4 | W_jobsec==5
{txt}(2,002 real changes made)
{com}. 
. 
. logit W_jobsec2 rti [pweight=weight] 

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5519.7187}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5467.2382}  
Iteration 2:{space 3}log pseudolikelihood = {res:-5466.2802}  
Iteration 3:{space 3}log pseudolikelihood = {res:-5466.2799}  
{res}
{txt}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,766}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:81.55}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}Log pseudolikelihood = {res:-5466.2799}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0097}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   W_jobsec2{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}rti {c |}{col 14}{res}{space 2} .3036476{col 26}{space 2} .0336245{col 37}{space 1}    9.03{col 46}{space 3}0.000{col 54}{space 4} .2377449{col 67}{space 3} .3695504
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.828694{col 26}{space 2} .0300665{col 37}{space 1}   94.08{col 46}{space 3}0.000{col 54}{space 4} 2.769765{col 67}{space 3} 2.887624
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{com}.  margins, atmeans at(rti=(-1.52(0.05)2.24)) 
{res}
{txt}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,766}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(W_jobsec2), predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.52}}
{lalign 8:2._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.47}}
{lalign 8:3._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.42}}
{lalign 8:4._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.37}}
{lalign 8:5._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.32}}
{lalign 8:6._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.27}}
{lalign 8:7._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.22}}
{lalign 8:8._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.17}}
{lalign 8:9._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.12}}
{lalign 8:10._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.07}}
{lalign 8:11._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-1.02}}
{lalign 8:12._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.97}}
{lalign 8:13._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.92}}
{lalign 8:14._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.87}}
{lalign 8:15._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.82}}
{lalign 8:16._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.77}}
{lalign 8:17._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.72}}
{lalign 8:18._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.67}}
{lalign 8:19._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.62}}
{lalign 8:20._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.57}}
{lalign 8:21._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.52}}
{lalign 8:22._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.47}}
{lalign 8:23._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.42}}
{lalign 8:24._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.37}}
{lalign 8:25._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.32}}
{lalign 8:26._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.27}}
{lalign 8:27._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.22}}
{lalign 8:28._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.17}}
{lalign 8:29._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.12}}
{lalign 8:30._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.07}}
{lalign 8:31._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:-.02}}
{lalign 8:32._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.03}}
{lalign 8:33._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.08}}
{lalign 8:34._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.13}}
{lalign 8:35._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.18}}
{lalign 8:36._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.23}}
{lalign 8:37._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.28}}
{lalign 8:38._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.33}}
{lalign 8:39._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.38}}
{lalign 8:40._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.43}}
{lalign 8:41._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.48}}
{lalign 8:42._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.53}}
{lalign 8:43._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.58}}
{lalign 8:44._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.63}}
{lalign 8:45._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.68}}
{lalign 8:46._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.73}}
{lalign 8:47._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.78}}
{lalign 8:48._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.83}}
{lalign 8:49._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.88}}
{lalign 8:50._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.93}}
{lalign 8:51._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:.98}}
{lalign 8:52._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.03}}
{lalign 8:53._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.08}}
{lalign 8:54._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.13}}
{lalign 8:55._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.18}}
{lalign 8:56._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.23}}
{lalign 8:57._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.28}}
{lalign 8:58._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.33}}
{lalign 8:59._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.38}}
{lalign 8:60._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.43}}
{lalign 8:61._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.48}}
{lalign 8:62._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.53}}
{lalign 8:63._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.58}}
{lalign 8:64._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.63}}
{lalign 8:65._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.68}}
{lalign 8:66._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.73}}
{lalign 8:67._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.78}}
{lalign 8:68._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.83}}
{lalign 8:69._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.88}}
{lalign 8:70._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.93}}
{lalign 8:71._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:1.98}}
{lalign 8:72._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.03}}
{lalign 8:73._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.08}}
{lalign 8:74._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.13}}
{lalign 8:75._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.18}}
{lalign 8:76._at: }{space 0}{lalign 3:rti} = {res:{ralign 5:2.23}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .9142878{col 26}{space 2}  .003851{col 37}{space 1}  237.42{col 46}{space 3}0.000{col 54}{space 4}   .90674{col 67}{space 3} .9218356
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .9154701{col 26}{space 2} .0036967{col 37}{space 1}  247.64{col 46}{space 3}0.000{col 54}{space 4} .9082246{col 67}{space 3} .9227156
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9166376{col 26}{space 2} .0035472{col 37}{space 1}  258.41{col 46}{space 3}0.000{col 54}{space 4} .9096852{col 67}{space 3} .9235899
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .9177904{col 26}{space 2} .0034024{col 37}{space 1}  269.75{col 46}{space 3}0.000{col 54}{space 4} .9111219{col 67}{space 3} .9244589
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .9189287{col 26}{space 2} .0032623{col 37}{space 1}  281.68{col 46}{space 3}0.000{col 54}{space 4} .9125346{col 67}{space 3} .9253227
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .9200526{col 26}{space 2} .0031271{col 37}{space 1}  294.22{col 46}{space 3}0.000{col 54}{space 4} .9139235{col 67}{space 3} .9261816
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .9211622{col 26}{space 2} .0029968{col 37}{space 1}  307.38{col 46}{space 3}0.000{col 54}{space 4} .9152886{col 67}{space 3} .9270359
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .9222578{col 26}{space 2} .0028715{col 37}{space 1}  321.18{col 46}{space 3}0.000{col 54}{space 4} .9166297{col 67}{space 3} .9278858
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .9233394{col 26}{space 2} .0027513{col 37}{space 1}  335.61{col 46}{space 3}0.000{col 54}{space 4}  .917947{col 67}{space 3} .9287318
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .9244072{col 26}{space 2} .0026362{col 37}{space 1}  350.66{col 46}{space 3}0.000{col 54}{space 4} .9192404{col 67}{space 3}  .929574
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .9254613{col 26}{space 2} .0025264{col 37}{space 1}  366.32{col 46}{space 3}0.000{col 54}{space 4} .9205097{col 67}{space 3} .9304129
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .9265019{col 26}{space 2}  .002422{col 37}{space 1}  382.54{col 46}{space 3}0.000{col 54}{space 4} .9217549{col 67}{space 3} .9312488
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .9275291{col 26}{space 2} .0023231{col 37}{space 1}  399.27{col 46}{space 3}0.000{col 54}{space 4} .9229759{col 67}{space 3} .9320822
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .928543{col 26}{space 2} .0022298{col 37}{space 1}  416.42{col 46}{space 3}0.000{col 54}{space 4} .9241726{col 67}{space 3} .9329134
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .9295438{col 26}{space 2} .0021424{col 37}{space 1}  433.88{col 46}{space 3}0.000{col 54}{space 4} .9253448{col 67}{space 3} .9337429
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .9305317{col 26}{space 2} .0020609{col 37}{space 1}  451.51{col 46}{space 3}0.000{col 54}{space 4} .9264924{col 67}{space 3}  .934571
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .9315067{col 26}{space 2} .0019855{col 37}{space 1}  469.15{col 46}{space 3}0.000{col 54}{space 4} .9276152{col 67}{space 3} .9353983
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .9324691{col 26}{space 2} .0019163{col 37}{space 1}  486.60{col 46}{space 3}0.000{col 54}{space 4} .9287132{col 67}{space 3}  .936225
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .9334189{col 26}{space 2} .0018534{col 37}{space 1}  503.62{col 46}{space 3}0.000{col 54}{space 4} .9297862{col 67}{space 3} .9370515
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .9343562{col 26}{space 2}  .001797{col 37}{space 1}  519.96{col 46}{space 3}0.000{col 54}{space 4} .9308343{col 67}{space 3} .9378782
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .9352813{col 26}{space 2}  .001747{col 37}{space 1}  535.38{col 46}{space 3}0.000{col 54}{space 4} .9318573{col 67}{space 3} .9387053
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .9361943{col 26}{space 2} .0017035{col 37}{space 1}  549.59{col 46}{space 3}0.000{col 54}{space 4} .9328556{col 67}{space 3}  .939533
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .9370952{col 26}{space 2} .0016664{col 37}{space 1}  562.35{col 46}{space 3}0.000{col 54}{space 4} .9338291{col 67}{space 3} .9403613
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .9379842{col 26}{space 2} .0016357{col 37}{space 1}  573.44{col 46}{space 3}0.000{col 54}{space 4} .9347783{col 67}{space 3} .9411902
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .9388615{col 26}{space 2} .0016112{col 37}{space 1}  582.70{col 46}{space 3}0.000{col 54}{space 4} .9357036{col 67}{space 3} .9420195
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .9397272{col 26}{space 2} .0015928{col 37}{space 1}  589.99{col 46}{space 3}0.000{col 54}{space 4} .9366055{col 67}{space 3}  .942849
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .9405814{col 26}{space 2} .0015801{col 37}{space 1}  595.27{col 46}{space 3}0.000{col 54}{space 4} .9374845{col 67}{space 3} .9436783
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .9414243{col 26}{space 2} .0015728{col 37}{space 1}  598.55{col 46}{space 3}0.000{col 54}{space 4} .9383416{col 67}{space 3}  .944507
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .9422559{col 26}{space 2} .0015707{col 37}{space 1}  599.91{col 46}{space 3}0.000{col 54}{space 4} .9391775{col 67}{space 3} .9453344
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .9430765{col 26}{space 2} .0015732{col 37}{space 1}  599.46{col 46}{space 3}0.000{col 54}{space 4}  .939993{col 67}{space 3} .9461599
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .9438861{col 26}{space 2} .0015801{col 37}{space 1}  597.37{col 46}{space 3}0.000{col 54}{space 4} .9407892{col 67}{space 3} .9469829
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .9446848{col 26}{space 2} .0015908{col 37}{space 1}  593.85{col 46}{space 3}0.000{col 54}{space 4} .9415669{col 67}{space 3} .9478027
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .9454728{col 26}{space 2}  .001605{col 37}{space 1}  589.09{col 46}{space 3}0.000{col 54}{space 4} .9423271{col 67}{space 3} .9486185
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.      marginsplot , recast(line) recastci(rline) ci1opts(fintensity(50) lpattern(dot)) xti(Risk of automation) yti("Predicted probability (95% CI)") ti("Importance of job security") saving("Figure/security.gph", replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:rti}{p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Figure/security.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Figure/security.gph} saved
{com}. {c )-}
. * Combine the specified graphs into one figure
. graph combine "Figure/security.gph" "Figure/difficult.gph" "Figure/losing.gph" "Figure/dissatisfied.gph"
{res}{com}. 
. * Export the combined graph as a PDF file, replacing any existing file
. graph export "Figure/jobdisatisfactionpredictedtogetherall.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
Figure/jobdisatisfactionpredictedtogetherall.pdf{rm}
saved as
PDF
format
{p_end}
{com}. 
. * Delete the individual graph files after combining them
. erase "Figure/security.gph"
. erase "Figure/difficult.gph"
. erase "Figure/losing.gph"
. erase "Figure/dissatisfied.gph"
. 
. {c )-}
{txt}
{com}. 
{txt}end of do-file

{com}. exit, clear
